sequential vs parallelization (WDPA)
Om Prakash Bhandari
5/18/2022
Last updated: 2022-06-10
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Knit directory: masters-thesis/
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Introduction
In this study, we are going to check how the processing time will differ - (1) sequential approach (2) parallel approach while processing rasters with varying resolutions and for different study areas:
- wdpa polygons (167)
- simplified wdpa polygons (152)
- fishnet gridded cells (14338)
Let’s see how these three different aoi polygons look like:
my_mapFour raster objects with varying resolutions were generated across the Bolivia region to carry out this study. The details of the rasters thus created follows:
- 0.1 degree (~11.1 km) = size 71.7 KB
- 0.01 degree (~1.11 km) = size 6.9 MB
- 0.001 degree (~111 m) = size 691.9 MB
- 0.0005 degree (~55.5 m) = size 2.7 GB
Results
To carry out this analysis, all the results are aggregated and group by raster resolutions. Thus, for specific raster resolution, we could recommend what could be the most suitable combination.
Conclusion
| Rasters | size | Combination of functions and methods |
|---|---|---|
| 0.1 degree (~11.1 km) | 71.7 KB | 1. terra zonal in sequential mode |
| 0.01 degree (~1.11 km) | 6.9 MB | 1. terra zonal in sequential mode 2. exact extract in sequential mode |
| 0.001 degree (~111 m) | 691.9 MB | 1. for wdpa polygons, exact extract with 16 cores 2. for fishnets, exact extract sequential |
| 0.0005 degree (~55.5 m) | 2.7 GB | 1. for wdpa polygons, exact extract with 16 cores 2. for fishnets, exact extract sequential |
terra zonal did not process very small polygons in many occasions, so if your study area contains very small areas, it is recommended not to opt terra zonal.
sessionInfo()R version 3.6.3 (2020-02-29)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.6 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.2.20.so
locale:
[1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8
[4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8
[7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C
[10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] magrittr_2.0.3 rmarkdown_2.11 plotly_4.9.3
[4] RColorBrewer_1.1-2 htmltools_0.5.2 scales_1.1.1
[7] ggsci_2.9 leaflet.extras2_1.1.0 leaflet.extras_1.0.0
[10] exactextractr_0.7.2 leaflet_2.0.4.1 forcats_0.5.1
[13] stringr_1.4.0 dplyr_1.0.9 purrr_0.3.4
[16] readr_2.1.2 tibble_3.1.7 tidyverse_1.3.1
[19] ggplot2_3.3.4 tidyr_1.2.0 terra_1.5-21
[22] sf_1.0-7 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] fs_1.5.0 lubridate_1.7.10 httr_1.4.3
[4] rprojroot_2.0.2 tools_3.6.3 backports_1.2.1
[7] bslib_0.2.5.1 utf8_1.2.2 R6_2.5.1
[10] KernSmooth_2.23-20 lazyeval_0.2.2 DBI_1.1.2
[13] colorspace_2.0-1 raster_3.5-12 withr_2.4.2
[16] sp_1.4-6 tidyselect_1.1.2 processx_3.5.2
[19] compiler_3.6.3 git2r_0.28.0 cli_3.3.0
[22] rvest_1.0.2 xml2_1.3.3 labeling_0.4.2
[25] sass_0.4.0 classInt_0.4-3 callr_3.7.0
[28] proxy_0.4-26 digest_0.6.29 pkgconfig_2.0.3
[31] highr_0.8 dbplyr_2.1.1 fastmap_1.1.0
[34] htmlwidgets_1.5.3 rlang_1.0.2 readxl_1.3.1
[37] rstudioapi_0.13 farver_2.1.0 jquerylib_0.1.4
[40] generics_0.1.2 jsonlite_1.8.0 crosstalk_1.1.1
[43] Rcpp_1.0.8.3 munsell_0.5.0 fansi_1.0.3
[46] lifecycle_1.0.1 stringi_1.7.6 whisker_0.4
[49] yaml_2.2.1 grid_3.6.3 promises_1.2.0.1
[52] crayon_1.5.1 lattice_0.20-45 haven_2.3.1
[55] hms_1.1.1 knitr_1.34 ps_1.5.0
[58] pillar_1.7.0 codetools_0.2-18 reprex_2.0.0
[61] glue_1.6.2 evaluate_0.14 getPass_0.2-2
[64] leaflet.providers_1.9.0 data.table_1.14.2 modelr_0.1.8
[67] vctrs_0.4.1 tzdb_0.1.2 httpuv_1.6.1
[70] cellranger_1.1.0 gtable_0.3.0 assertthat_0.2.1
[73] xfun_0.24 broom_0.7.6 e1071_1.7-9
[76] later_1.2.0 viridisLite_0.4.0 class_7.3-20
[79] units_0.8-0 ellipsis_0.3.2